doi: 10.3934/dcdss.2020253

Mathematical model construction of robot obstacle avoidance route optimization based on SLAM algorithm

1. 

School of Mechanical and Material Engineering, Xi'an University, Xi'an 710065, China

2. 

Xi'an Institute of Applied Optics, Xi'an 710065, China

* Corresponding author: Jing Sun

Received  April 2019 Revised  May 2019 Published  January 2020

In order to improve the recognition accuracy and operation efficiency of obstacle avoidance routes of robots and obtain the best obstacle avoidance routes, a mathematical model of obstacle avoidance routes optimization based on SLAM algorithm is constructed. Two behavioral dynamics models of course angle dynamics and velocity dynamics are constructed. Rolling window sensor is used for route planning. Active SLAM algorithm framework is established by combining behavioral dynamics and rolling window route planning method with SLAM. The algorithm uses local sub-maps to merge rolling window map with global map, and updates the machine. On this basis, the SLAM method based on particle filter is used to construct the optimization mathematical model of the obstacle avoidance route of the robot, which can realize the effective planning of the obstacle avoidance route of the robot and obtain the optimal obstacle avoidance path. The experimental results show that the proposed model has the advantages of high recognition accuracy, good anti-interference performance and short running time. The proposed model almost coincides with the actual shortest path. The recognition accuracy is higher than 98$ \% $, and the total motion time is only 460 s at 10 target points.

Citation: Jing Sun, Senlin Yang, Wei Chen, Wei Zhang, Xia Liu. Mathematical model construction of robot obstacle avoidance route optimization based on SLAM algorithm. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020253
References:
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Z. XuY. LinJ. Yao and T. Guo, Target search path fuzzy control of robot navigation, Computer Simulation, 33 (2016), 300-304.   Google Scholar

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show all references

References:
[1]

S. GhoshP. K. Panigrahi and D. R. Parhi, Analysis of fpa and ba meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment, Iet Science Measurement and Technology, 11 (2017), 817-828.  doi: 10.1049/iet-smt.2016.0273.  Google Scholar

[2]

M. GuerraD. Efimov and Z. Gang, Finite-time obstacle avoidance for unicycle-like robot subject to additive input disturbances, Autonomous Robots, 41 (2017), 19-30.  doi: 10.1007/s10514-015-9526-0.  Google Scholar

[3]

H. Kim and J. Min, Electric field control of bacteria-powered microrobots using a static obstacle avoidance algorithm, IEEE Transactions on Robotics, 32 (2016), 125-137.  doi: 10.1109/TRO.2015.2504370.  Google Scholar

[4]

F. LiG. Liang and X. Du, Research on intelligent robotics for hazard identification based on image processing technology, Automation and Instrumentation, 6 (2017), 10-12.   Google Scholar

[5]

X. Lv and C. Han, Asynchronous motor vector control of fuel cell welding robot, Chinese Journal of Power Sources, 40 (2016), 1023-1026.   Google Scholar

[6]

D. SakaiH. Fukushima and F. Matsuno, Flocking for multirobots without distinguishing robots and obstacles, IEEE Transactions on Control Systems Technology, 25 (2017), 1019-1027.  doi: 10.1109/TCST.2016.2581148.  Google Scholar

[7]

M. SederM. Baoti and I. Petrovi, Receding horizon control for convergent navigation of a differential drive mobile robot, IEEE Transactions on Control Systems Technology, 25 (2017), 653-660.  doi: 10.1109/TCST.2016.2558479.  Google Scholar

[8]

R. ShiD. Jiang and X. Li, Platform self-location and attitude measure technique based on direction finding for radiation objects, Journal of China Academy of Electronics and Information Technology, 11 (2016), 73-78.   Google Scholar

[9]

C. WangW. Xu and B. Yin, Path planning in dynamic environment based on improved shuffled frog leaping algorithm, Journal of Jilin University (Science Edition), 54 (2016), 857-861.   Google Scholar

[10]

M. WangJ. Luo and U. Walter, A non-linear model predictive controller with obstacle avoidance for a space robot, Advances in Space Research, 57 (2016), 1737-1746.  doi: 10.1016/j.asr.2015.06.012.  Google Scholar

[11]

S. WangJ. Li and L. Shang, Non-four-wire lithium battery pack voltage detecting correction method study, Journal of Power Supply, 14 (2016), 80-85.   Google Scholar

[12]

B. Wei and K. Ren, A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved rrt algorithm, Sensors, 18 (2018), 571.   Google Scholar

[13]

Z. XuY. LinJ. Yao and T. Guo, Target search path fuzzy control of robot navigation, Computer Simulation, 33 (2016), 300-304.   Google Scholar

[14]

L. Yang, H. Li and Z. Gao, Obstacle avoidance path planning of hybrid harvesting manipulator based on joint configuration space, Transactions of the Chinese Society of Agricultural Engineering. Google Scholar

[15]

X. YangH. Fan and P. Shi, Nonlinear control for tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint, IEEE Transactions on Control Systems Technology, 24 (2016), 741-746.   Google Scholar

Figure 1.  Active SLAM algorithm block diagram
Figure 2.  Map diagram of the scrolling window
Figure 3.  Relationship between global coordinate system and rolling window coordinate system
Figure 4.  Distribution of obstacles in the simulation map
Figure 5.  Different model robot roadmaps for single target points
Figure 6.  The robot of the three models moves in two target points
Figure 7.  Robot speed change
Figure 8.  Robot heading angle change
Figure 9.  Experimental environment map
Figure 10.  Distribution of obstacles in simulation environment
Figure 11.  Robot motion route by using this model
Figure 12.  Robot motion route by using adopt geothreshold model
Figure 13.  Robot motion route by using SAO model
Figure 14.  Comparison of robot recognition precision of different models
Figure 15.  Comparison of time of robot experiments in different models
Figure 16.  Comparison of time of robot experiments in different models
Table 1.  Robots at 10 target points
Target point Using this model robot Adopt geothreshold model robot Using SAO Model Robots
Motion length/m Exercise time/s Motion length/m Exercise time/s Motion length/m Exercise time/s
0-1 47 49 58 62 61 65
1-2 27 35 35 52 42 58
2-3 36 42 42 66 52 71
3-4 55 57 61 72 59 70
4-5 45 47 58 53 63 55
5-6 27 36 36 51 42 49
6-7 35 41 55 66 63 65
7-8 85 62 108 75 127 70
8-9 26 33 35 55 41 56
9-10 74 58 99 88 105 90
total 457 460 587 640 655 649
Target point Using this model robot Adopt geothreshold model robot Using SAO Model Robots
Motion length/m Exercise time/s Motion length/m Exercise time/s Motion length/m Exercise time/s
0-1 47 49 58 62 61 65
1-2 27 35 35 52 42 58
2-3 36 42 42 66 52 71
3-4 55 57 61 72 59 70
4-5 45 47 58 53 63 55
5-6 27 36 36 51 42 49
6-7 35 41 55 66 63 65
7-8 85 62 108 75 127 70
8-9 26 33 35 55 41 56
9-10 74 58 99 88 105 90
total 457 460 587 640 655 649
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